Methods and apparatus for providing context aware personalized in-store customer experience

ABSTRACT

The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer&#39;s physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer&#39;s online interaction with the retailer. Upon detecting the customer&#39;s presence at the retailer&#39;s physical location, data of the customer&#39;s online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer&#39;s physical location.

TECHNICAL FIELD

The disclosed subject matter relates generally to personalizing anin-store experience based on the type of web based interactions of thecustomer.

BACKGROUND

Commercial websites and applications often provide recommendations totheir users. These recommendations include content related to thecurrent web page or application being accessed by the user (e.g.,related news articles), products related to the product in the user'sshopping cart (e.g., the user purchases shoes). If so, it may includerecommendations or advertisements/advertisements related to the currentweb page being accessed by the user (e.g. sock recommendations) and/orthe product in the user's shopping cart (e.g. shoes). Product and offerrecommendations may also be submitted to email communications sent tothe user. Personalized, relevant, and appropriate recommendations canhelp increase user traffic, sales, and/or revenue, and are therefore akey component of commercial websites and applications.

Operators of commercial websites however fail to recognize that theinformation gathered online may be advantageously tailored topersonalize an in-store visit as well. Specifically, the informationgathered from the user's interaction with the web page or app, may beused with data related to a specific store, or area of which the user isdetermined to be within proximity to. In other words, some of the sameinformation which enables webpages to make recommendations to the user,may also be used to influence in-store behavior of the user, takingaccount of the temporal relationship between the gathered informationand the users in-store visit. For example, the recommendation forpurchase of socks described above would advantageously be tied to theinventory of the current store in which the user is located, or the samerecommendation could be altered if the recommended pair of socks is notin stock at the particular store.

The temporal relationship of the prior information gathered from theuser and the current in-store visit is another parameter that may beused to guide or personalize the user's in-store experience.

SUMMARY

The embodiments described herein are directed to a system and method forpersonalizing a customer's in-store experience. Personalizing thecustomer's in-store experience is advantageous in that it has been shownto be more influential that generic marketing and thus increased salesand allows among other things shaping customer traffic to aisles fortargeted items and local/seasonal/geo-based customer centric targeting.In addition to or instead of the advantages presented herein, persons ofordinary skill in the art would recognize and appreciate otheradvantages as well.

In accordance with various embodiments, exemplary systems may beimplemented in any suitable hardware or hardware and software, such asin any suitable computing device.

In some embodiments, the system includes a customer location beacon; amobile device associated with the customer; a database; a communicationsystem; and a computing device connected to the database, customerlocation beacon and the communication system. The computing device isconfigured to receive a notification of the customer's presence at theretailer's physical location from the customer location beacon, andtriggered by the notification access the database(s) for the customer'sprior or current online interactions with or available to the retailer.The computing device classifies the customer's online interactions intoone of several of classifications, based in part on the temporalcharacteristic of those interactions. The computing device in theseembodiments is also configured to access the database for retailerinformation associated with the visited store or retailer's physicallocation, and select push content based upon the classification and theretailer information associated with the visited store; and transmit theselected push content to the customer's phone or other mobile device.

In other embodiments, a method is provided that personalizes customerexperience at a retailer's physical location based upon classificationof customer's prior and current online interactions. The methodincluding determining the customer's presence at the retailer's physicallocation, in response accessing the customer's online interactions withor available to the retailer and classifying the customer's recentonline interactions into one of a plurality of classifications. Themethod also includes accessing retailer information associated with thespecific physical location; selecting push content based upon theclassification and the retailer information, and transmitting the pushcontent to the customer's phone.

In yet other embodiments, a non-transitory computer readable mediumhaving instructions stored thereon is provided. The instructions, whenexecuted by at least one processor, cause a device to perform operationsincluding determining the customer's presence at the retailer's physicallocation, accessing data of the customer's recent online interactionsand retailer information associated with the retailer's physicallocation and classifying the customer's online interactions. Theinstructions also include selecting push content based upon theclassification of the customer's interactions and the retailerinformation; and transmission of the selected push content to thecustomer's mobile device. The push content is also selected based on theobject of the customer's online interaction given a firstclassification, selected based on features from the retailer informationgiven a second classification and selected based on the object of anin-store search in a third classification.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the present disclosures will be morefully disclosed in, or rendered obvious by the following detaileddescriptions of example embodiments. The detailed descriptions of theexample embodiments are to be considered together with the accompanyingdrawings wherein like numbers refer to like parts and further wherein:

FIG. 1 is a block diagram of communication network used to personalizecustomer experience in accordance with some embodiments;

FIG. 2 is a block diagram of the in-store experience personalizationcomputing device of the communication system of FIG. 1 in accordancewith some embodiments;

FIG. 3 is a diagram of a system for personalizing a customer's in-storeexperience in accordance with embodiments of the disclosed subjectmatter;

FIG. 4 is a flowchart of operations carried out by the in-storeexperience personalization computing device in accordance withembodiments of the disclosed subject matter: and,

FIG. 5 is a flowchart of a method for personalizing customer experienceat a retailer's physical location based upon classification ofcustomer's online interaction with the retailer in accordance withembodiments of the disclosed subject matter.

DETAILED DESCRIPTION

The description of the preferred embodiments is intended to be read inconnection with the accompanying drawings, which are to be consideredpart of the entire written description of these disclosures. While thepresent disclosure is susceptible to various modifications andalternative forms, specific embodiments are shown by way of example inthe drawings and will be described in detail herein. The objectives andadvantages of the claimed subject matter will become more apparent fromthe following detailed description of these exemplary embodiments inconnection with the accompanying drawings.

It should be understood, however, that the present disclosure is notintended to be limited to the particular forms disclosed. Rather, thepresent disclosure covers all modifications, equivalents, andalternatives that fall within the spirit and scope of these exemplaryembodiments. The terms “couple,” “coupled,” “operatively coupled,”“operatively connected,” and the like should be broadly understood torefer to connecting devices or components together either mechanically,electrically, wired, wirelessly, or otherwise, such that the connectionallows the pertinent devices or components to operate (e.g.,communicate) with each other as intended by virtue of that relationship.

Turning to the drawings, FIG. 1 illustrates a block diagram of acommunication system 100 that includes an in-store experiencepersonalization computing device 102 (e.g., a server, such as anapplication server), a web server 104, and database 116, and multiplecustomer computing devices 110, 112, 114 operatively coupled overnetwork 118.

An in-store experience personalization computing device 102, server 104,and multiple customer computing devices 110, 112, 114 can each be anysuitable computing device that includes any hardware or hardware andsoftware combination for processing and handling information. Forexample, each can include one or more processors, one or morefield-programmable gate arrays (FPGAs), one or more application-specificintegrated circuits (ASICs), one or more state machines, digitalcircuitry, or any other suitable circuitry. In addition, each cantransmit data to, and receive data from, or through the communicationnetwork 118.

In some examples, the in-store experience personalization computingdevice 102 can be a computer, a workstation, a laptop, a server such asa cloud-based server, or any other suitable device. In some examples,each of multiple customer computing devices 110, 112, 114 can be acellular phone, a smart phone, a tablet, a personal assistant device, avoice assistant device, a digital assistant, a laptop, a computer, orany other suitable device. In some examples, in-store experiencepersonalization computing device 102, and web server 104 are operated bya retailer, and multiple customer computing devices 112, 114 areoperated by customers of the retailer.

Although FIG. 1 illustrates three customer computing devices 110, 112,114, the communication system 100 used for in-store personalization caninclude any number of customer computing devices 110, 112, 114.Similarly, the communication system 100 can include any number ofworkstation(s) (not shown), in-store experience personalizationcomputing devices 102, web servers 104, and databases 116.

The in-store experience personalization computing device 102 is operableto communicate with database 116 directly or over communication network118. For example, in-store experience personalization computing device102 can store data to, and read data from, database 116. Database 116may be remote storage devices, such as a cloud-based server, a disk(e.g., a hard disk), a memory device on another application server, anetworked computer, or any other suitable remote storage. Although shownremote to the in-store experience personalization computing device 102,in some examples, database 116 may be a local storage device, such as ahard drive, a non-volatile memory, or a USB stick. The in-storeexperience personalization computing device 102 may store data fromworkstations or the web server 104 in database 116. In some examples,storage devices store instructions that, when executed by in-storeexperience personalization computing device 102, allow intent freeanswering computing device 102 to determine one or more s results inresponse to a user query.

Communication network 118 can be a WiFi® network, a cellular networksuch as a 3GPP® network, a Bluetooth® network, a satellite network, awireless local area network (LAN), a network utilizing radio-frequency(RF) communication protocols, a Near Field Communication (NFC) network,a wireless Metropolitan Area Network (MAN) connecting multiple wirelessLANs, a wide area network (WAN), or any other suitable network.Communication network 118 can provide access to, for example, theInternet.

FIG. 2 illustrates the in-store experience personalization computingdevice 102 of FIG. 1. The in-store experience personalization computingdevice 102 may include one or more processors 201, working memory 202,one or more input/output devices 203, instruction memory 207, atransceiver 204, one or more communication ports 207, and a display 206,all operatively coupled to one or more data buses 208. Data buses 208allow for communication among the various devices. Data buses 208 caninclude wired, or wireless, communication channels.

Processors 201 can include one or more distinct processors, each havingone or more processing cores. Each of the distinct processors can havethe same or different structure. Processors 201 can include one or morecentral processing units (CPUs), one or more graphics processing units(GPUs), application specific integrated circuits (ASICs), digital signalprocessors (DSPs), and the like.

Processors 201 can be configured to perform a certain function oroperation by executing code, stored on instruction memory 207, embodyingthe function or operation. For example, processors 201 can be configuredto perform one or more of any function, method, or operation disclosedherein.

Instruction memory 207 can store instructions that can be accessed(e.g., read) and executed by processors 201. For example, instructionmemory 207 can be a non-transitory, computer-readable storage mediumsuch as a read-only memory (ROM), an electrically erasable programmableread-only memory (EEPROM), flash memory, a removable disk, CD-ROM, anynon-volatile memory, or any other suitable memory.

Processors 201 can store data to, and read data from, working memory202. For example, processors 201 can store a working set of instructionsto working memory 202, such as instructions loaded from instructionmemory 207. Processors 201 can also use working memory 202 to storedynamic data created during the operation of intent free answeringcomputing device 102. Working memory 202 can be a random access memory(RAM) such as a static random access memory (SRAM) or dynamic randomaccess memory (DRAM), or any other suitable memory.

Input-output devices 203 can include any suitable device that allows fordata input or output. For example, input-output devices 203 can includeone or more of a keyboard, a touchpad, a mouse, a stylus, a touchscreen,a physical button, a speaker, a microphone, or any other suitable inputor output device.

Communication port(s) 209 can include, for example, a serial port suchas a universal asynchronous receiver/transmitter (UART) connection, aUniversal Serial Bus (USB) connection, or any other suitablecommunication port or connection. In some examples, communicationport(s) 209 allows for the programming of executable instructions ininstruction memory 207. In some examples, communication port(s) 209allow for the transfer (e.g., uploading or downloading) of data, such asmachine learning algorithm training data.

Display 206 can display user interface 205. User interfaces 205 canenable user interaction with in-store experience personalizationcomputing device 102. In some examples, a user can interact with userinterface 205 by engaging input-output devices 203. In some examples,display 206 can be a touchscreen, where user interface 205 is displayedby the touchscreen.

Transceiver 204 allows for communication with a network, such as thecommunication network 118 of FIG. 1. For example, if communicationnetwork 118 of FIG. 1 is a cellular network, transceiver 204 isconfigured to allow communications with the cellular network. In someexamples, transceiver 204 is selected based on the type of communicationnetwork 118 in-store experience personalization computing device 102will be operating in. Processor(s) 201 is operable to receive data from,or send data to, a network, such as communication network 118 of FIG. 1,via transceiver 204.

FIG. 3 illustrates a diagram 300 of a system for personalizing acustomer's in-store experience. The in-store experience personalizationcomputing device 302 is operably connected to data stores (databases)315 and 316, these data stores are shown separately for illustrationonly to reflect different categories of data accessible by the in-storeexperience personalization computing device 302, however it is alsoenvisioned that the data stores may be unitary. Data stores include realtime and historical customer on-line interactions 315 and retailerinformation 316.

Store beacon 320 detects the presence of a customer at the retailer'sphysical location. The store beacons interact with customer's mobiledevice to determine its proximity to the retailer's physical location inorder to determine the customer's presence. Location data from thecustomer may be determined by information provided to the beacon via aretailer app, connection to local network, or a VLR (visiting locationregister).

The in-store experience personalization computing device 302 is alsooperably connected to a notification system 330. The notification system330 communicates through the retailer app on the user's mobile device(phone) 312, or other application, such as SMS, Social Media or othercommunication platform. The notification system 330 receives pushnotifications from the in-store experience personalization computingdevice 302 and relays the notifications to the customer's mobile device312.

The flowchart of FIG. 4 illustrates several paths for thepersonalization of the customer's in-store experience, each dependentupon the customer's current or prior online interactions. In oneexample, the customer has searched and browsed online for items relatedto a type of product (e.g. a bread machine) five day prior to the visit.The customer however in this example did not purchase the product. Uponvisiting the retailer's physical location, specifically store X as shownin Block 401, the store location beacons are fired. Customer informationand retailer information is retrieved from database 116 as shown inBlock 403. The customer information is associated with the customerspast session data over a predetermined time, (e.g. in the past 7 days)is fetched from the database 116. Other customer information may also beretrieved, such as customer brand affinity and price conciseness. Theretailer information associated with the physical location is alsoretrieved from the database 116, this information may include inventory,product descriptions, product locations, store layout, local productpreferences, and other features associated with store X.

As shown in decision block 405, it is determined whether the customershas recent online activity within a predetermined time period. In thepresent example as noted above, the customer conducted an online searchfor a product 5 days prior, and thus in Block 407, the system 300selects a product(s) or type of products(s) based on the object of therecent online activity (interactions). The predetermined period may be aweek, ten days, two weeks or other appropriate period and may beadjustable with respect to the date(e.g. shorter period at holidays), aswell as with respect to past customer shopping patterns (e.g. visitsstore X once a month, and thus a 29 day period may be more advantageous)The product(s) selection may be combined with product location (aisleinformation for store X) as shown in Block 409 and the push notificationis then sent to the customer as shown in Block 450. In the case when thecustomer after the search purchased a bread machine in the recent onlineinteraction, the system advantageously may select related complimentaryitems for inclusion in the push notification, such as a bread slicer, oringredients for making bread. The push notifications may take the formof a pop up, SMS, email, or phone call. The push notification mayinclude several products (items) available at the store and ordered byrelevance, along with the aisle location within the store to guide thecustomer.

In another example, the customer visits store Y and performs an in-storesearch for a product (e.g. humidifier) using the retailer's applicationas shown in Block 421. Receiving an API call from the application, thein-store experience personalization computing device 102, accessing thecustomer's interaction history and retailer information, selectsproduct(s) as shown in Block 423 related to the subject of the in-storesearch, irrespective of the recent online interactions considered in theprior example. Based on the retrieved information, (e.g. customers priceconsciousness, popularity of specific products, store inventory, storesales etc.) the in-store experience personalization computing device 102selects a product(s) (e.g. a popular brand of humidifier). Theproduct(s) selection may be combined with product location (aisleinformation for store X) as shown in Block 409 and the push notificationis then sent to the customer as shown in Block 450.

In another example, a customer visits the retailer's physical location,however the customer has not had any significant activity over the priorpredetermined period, (e.g. two weeks), but has regular online activityover a second longer predetermined period (e.g. 2 years). Upon the visitto store X as shown in Block 401, the store location beacons are firedand the customer information and retailer information is retrieved fromdatabase 116 as shown in Block 403.

As shown in decision block 405, it is determined whether the customershas recent online activity within a predetermined time period. In thisexample as noted above, the customer has not had a recent onlineinteraction within the predetermined period. In decision block 415 it isdetermined whether the customer has any online interaction over the pasttwo years, as a result of a positive determination, products associatedwith features of store X are selected for the push notification as shownin Block 417. The features of store X may include items trending at thatstore, inventory, discounts, promotions etc. The product(s) selectionmay be combined with product location (aisle information for store X) asshown in Block 409 and the push notification is then sent to thecustomer as shown in Block 450.

In FIG. 5, the steps involved in personalizing the in-store experiencebased on online interaction is illustrated. The customer's presence atone of the retailer's physical locations is detected in Block 502,preferably by the store location beacon 320. The in-store experiencepersonalization computing device 102 receives an indication of thecustomer and the store visited, in response, data related to customeronline interactions with the retailer is retrieved as shown in Block504, based upon this information, the customer's online interactions areclassified into one of a plurality of classifications as shown in Block506. A first classification for online interactions within a prior firstpredetermined period, (e.g. within seven days, ten days, two weeks, amonth), a second classification being within a prior second longerpredetermined period, (e.g. within the last year or two years, or anyinteractions) and a third classification being a real time (i.e. duringthe customers visit) in-store search by the customer. Otherclassifications or sub classifications, based upon the type ofinteractions are also envisioned, such as customer clicks, views, add tocart, and/or purchases, or combinations thereof. The firstclassification in some embodiments results in the product (i.e. object)of the recent interactions being the basis for the products selected forthe push notification, the second classification results in the featuresof the specific store being the basis for the products selected for thepush notification and the third classification results in the object ofthe in-store search being the basis.

The in-store experience personalization computing device 102 alsoaccesses retailer information associated with the particular store(physical location) visited in Block 508, as noted this information mayinclude items trending at that store, inventory, out of stock items,discounts, promotions, product information, product location and localpreferences, among others. The retailer information may be storedcentrally for a plurality of stores or may be resident in eachindividual store. Based upon both the classification of the user's priorinteractions and the retailer information particular to the visitedstore, products and/or services available at the retailer's physicallocation are selected as shown in Block 510. The selections may beranked by relevance using additional customer historic information, aswell as the retailer information, and may be combined with productlocation (aisle information) and transmitted to the customer's mobiledevice 312 as a push notification as shown in Block 512. The pushnotification advantageously enables the retailer at least in somesituations to provide the customer with recommendations strongly relatedto their prior or current online interactions along with aislenavigation information directing the customer to the products relevantwhile they are actually in the store. The push notifications generatedusing the disclosed subject matter also allows for consideration of thecustomer's historic tendencies (brand infinity, price tolerance, etc.)to be utilized in marketing the customer while present in the store,irrespective of the customer's intent for visiting the retailer'sphysical location.

Although the methods described above are with reference to theillustrated flowcharts, it will be appreciated that many other ways ofperforming the acts associated with the methods can be used. Forexample, the order of some operations may be changed, and some of theoperations described may be optional.

In addition, the methods and system described herein can be at leastpartially embodied in the form of computer-implemented processes andapparatus for practicing those processes. The disclosed methods may alsobe at least partially embodied in the form of tangible, non-transitorymachine-readable storage media encoded with computer program code. Forexample, the steps of the methods can be embodied in hardware, inexecutable instructions executed by a processor (e.g., software), or acombination of the two. The media may include, for example, RAMs, ROMs,CD-ROMs, DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or anyother non-transitory machine-readable storage medium. When the computerprogram code is loaded into and executed by a computer, the computerbecomes an apparatus for practicing the method. The methods may also beat least partially embodied in the form of a computer into whichcomputer program code is loaded or executed, such that, the computerbecomes a special purpose computer for practicing the methods. Whenimplemented on a general-purpose processor, the computer program codesegments configure the processor to create specific logic circuits. Themethods may alternatively be at least partially embodied in applicationspecific integrated circuits for performing the methods.

While the disclosed subject matter is described using customer dataobtained from online interactions with the retailer associated with thevisited store, it is also envisioned that customer data from othersources, for instance interactions on search engines, or with otherretailer sites would also be useful in practicing the personalization ofthe customer's in-store experience. For example, if the customervisiting the retailer's store, had searched in the prior five days on ageneric search engine for a bread machine, or had visited a webpage forbread machines from manufacturer or other seller and that data wasavailable to the retailer, the data could be used in the same manner asthe described internal data.

The foregoing is provided for purposes of illustrating, explaining, anddescribing embodiments of these disclosures. Modifications andadaptations to these embodiments will be apparent to those skilled inthe art and may be made without departing from the scope or spirit ofthese disclosures.

What is claimed is:
 1. A system for personalizing customer experience ata retailer's physical location based upon classification of prior onlineinteraction with the retailer, comprising: a customer location beacon; amobile device associated with the customer; a database; a communicationsystem; a computing device operably connected to the database, customerlocation beacon and the communication system, the computing deviceconfigured to: receive a notification of the customer's presence at theretailer's physical location from the customer location beacon, accessthe database for the customer's online interactions with or available tothe retailer; classify the customer's online interactions into one of aplurality of classifications; access the database for retailerinformation associated with the physical location; select push contentbased upon at least the classification and the retailer information; andtransmit the selected push content to the mobile device.
 2. The systemof claim 1, wherein the computing device is further configure todetermine whether there are online interactions by the customer arewithin a predetermined amount of time prior to the customer's present atthe physical location as a basis to classify the customer's onlineinteractions.
 3. The system of claim 2, wherein the online interactionsare selected from the group consisting of search, add to cart, purchaseand views.
 4. The system of claim 2, wherein the predetermined amount oftime is selected from the group consisting of less than or equal to aweek, less than or equal to ten days, and less than or equal to a month.5. The system of claim 1, wherein the computing device is furtherconfigured to determine whether the customer initiated an in-storesearch for a product to classify the customer's interactions.
 6. Thesystem of claim 1, wherein the computing device is further configured todetermine if the customer has an online account associated with theretailer to classify the customer's interactions.
 7. The system of claim2, wherein the retailer information comprises information selected fromthe group consisting of current inventory, product information,inventory layout, promotions, local preferences and sales trendsassociated with the retailer's physical location.
 8. The system of claim2, wherein the computing device is further configured to select the pushcontent based on an object of the online interactions for a firstclassification, wherein the push content includes the object of theonline interactions.
 9. The system of claim 2, wherein the computingdevice is further configured to select the push content based on anobject of the online interactions, wherein the push content includes aproduct complimentary to the object of the online interactions.
 10. Amethod of personalizing customer experience at a retailer's physicallocation based upon classification of customer's online interaction,comprising: determining the customer's presence at the retailer'sphysical location, accessing data of the customer's online interactionswith or available to the retailer; classifying the customer's onlineinteractions into one of a plurality of classifications; accessingretailer information associated with the physical location; selectingpush content based upon at least the classification and the retailerinformation; and transmitting the selected push content to the customer.11. The Method of claim 10, wherein the step of classifying includesdetermining there are online interactions by the customer within apredetermine amount of time prior to the customer's present at thephysical location.
 12. The method of claim 11, wherein the onlineinteractions are selected from the group consisting of search, add tocart, purchase and views.
 13. The method of claim 11, wherein thepredetermined amount of time is selected from the group consisting ofless than or equal to a week, less than or equal to ten days, and lessthan or equal to a month.
 14. The method of claim 10, wherein the stepof classifying includes determining the customer initiated an in-storesearch for a product and selecting the push content based on the objectof the in-store search.
 15. The method of claim 10, wherein the step ofclassifying includes determining the customer has an online accountassociated with the retailer and selecting the push content based on theretailer information.
 16. The method of claim 11, wherein the retailerinformation comprises information selected from the group consisting ofconsisting of current inventory, product information, inventory layout,promotions, local preferences and sales trends associated with theretailer's physical location.
 17. The method of claim 11, wherein thestep of selecting push content is further based on an object of theonline interactions.
 18. The method of claim 17, wherein the pushcontent includes the object of the online interactions.
 19. The methodof claim 17, wherein the push content includes a product complimentaryto the object of the online interactions.
 20. A non-transitory computerreadable medium having instructions stored thereon, wherein theinstructions, when executed by at least one processor, cause a device toperform operations comprising: determining the customer's presence atthe retailer's physical location; accessing data of the customer'sonline interactions with or available to the retailer; classifying thecustomer's online interactions into one of a plurality ofclassifications; accessing retailer information associated with thephysical location; selecting push content based upon at least theclassification and the retailer information; and transmit the selectedpush content to the customer; wherein the selection of the push contentis based on the object of the customer's online interaction in a firstclassification, the selection of push content is based on features fromthe retailer information in a second classification and the selection ofpush content is based on the object of an in-store search in a thirdclassification.